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Record W2152929815 · doi:10.1093/aje/kwq324

Early Menarche Predicts Incidence of Asthma in Early Adulthood

2010· article· en· W2152929815 on OpenAlexafffundabout
Ban Al‐Sahab, Mazen J. Hamadeh, Chris I. Ardern, Hala Tamim

Bibliographic record

VenueAmerican Journal of Epidemiology · 2010
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsYork University
FundersYork University
KeywordsMenarcheMedicineAsthmaIncidence (geometry)DemographyConfidence intervalOdds ratioPediatricsLogistic regressionEpidemiologyInternal medicine

Abstract

fetched live from OpenAlex

The present study explores the effect of age at menarche on the incidence of asthma during early adulthood. The analysis was based on Canadian girls followed up from 8-11 to 18-21 years of age during the first 6 cycles (1994-2005) of the National Longitudinal Survey of Children and Youth. Early menarche was defined as 1 standard deviation less than the average age at menarche. Asthma occurrence after menarche was measured as asthma that was diagnosed by a health care professional. The authors used logistic regression to investigate the association between early menarche and incidence of asthma, adjusting for possible confounders. A total of 1,176 girls weighted to represent 352,345 Canadian girls were analyzed. The incidence of asthma after menarche was 11.2% (95% confidence interval: 8.3, 14.0). The onset of early menarche (<11.56 years of age) predicted postmenarcheal incidence of asthma; girls who matured early had more than twice the risk of developing asthma during early adulthood than did girls who matured at an average age (odds ratio, 2.34, 95% confidence interval: 1.19, 4.59). The present study provides partial insight into the worldwide rapid increase in asthma rates that coincides with the declining trends in menarcheal timing. Further studies within different contexts are warranted to assess the generalizability of these Canadian findings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.325
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations52
Published2010
Admission routes3
Has abstractyes

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